Voice communications systems have traditionally used single-microphone noise reduction (NR) algorithms to suppress noise and provide optimal audio quality. Such algorithms, which depend on statistical differences between speech and noise, provide effective suppression of stationary noise, particularly where the signal to noise ratio (SNR) is moderate to high. However, the algorithms are less effective where the SNR is very low.
Mobile devices, such as cellular telephones, are used in many diverse environments, such as train stations, airports, busy streets and bars. Traditional single-microphone NR algorithms do not work effectively in these environments where the noise is dynamic (or non-stationary), e.g., background speech, music, passing vehicles etc. In order to suppress dynamic noise and further optimize NR performance on stationary noise, multiple-microphone NR algorithms have been proposed to address the problem using spatial information. However, these are typically computationally intensive and therefore are not suited to use in embedded devices, where processing power and battery life are constrained.
Further challenges to noise reduction are introduced by the reducing size of devices, such as cellular telephones and Bluetooth® headsets. This reduction in size of a device generally increases the distance between the microphone and the mouth of the user and results in lower user speech power at the microphone (and therefore lower SNR).
Common reference numerals are used throughout the Figures to indicate similar features.